Openfoodfacts : analyse exploratoire des données¶

%matplotlib inline

import os
import zipfile
import urllib

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd
from pandas.plotting import scatter_matrix

import qgrid

FOOD_PATH = os.path.join("datasets", "openfoodfacts")
FOOD_TRANSFORMED_PATH_FILE = os.path.join(FOOD_PATH, "fr.openfoodfacts.org.products_transformed.csv")

import seaborn as sns
sns.set()

plt.rcParams["figure.figsize"] = [16,9] # Taille par défaut des figures de matplotlib

pd.set_option("display.max_columns", 1000)
pd.set_option("display.max_rows",1000)

Import des données¶

import pandas as pd

def load_food_data(csv_path=FOOD_TRANSFORMED_PATH_FILE):
    return pd.read_csv(csv_path, sep=',', header=0, encoding='utf-8', low_memory=False)

food = load_food_data()

Informations globales sur les données¶

food.head()

qgrid.show_grid(food, grid_options={'forceFitColumns': False, 'defaultColumnWidth': 150})

food.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 98440 entries, 0 to 98439
Data columns (total 34 columns):
code                           98440 non-null object
last_modified_t                98440 non-null int64
product_name                   91247 non-null object
states_tags                    98440 non-null object
main_category_fr               61955 non-null object
brands                         86426 non-null object
brands_tags                    86422 non-null object
nutrition_grade_fr             61415 non-null object
energy_100g                    64593 non-null float64
sugars_100g                    62515 non-null float64
salt_100g                      62574 non-null float64
saturated-fat_100g             62375 non-null float64
fiber_100g                     45723 non-null float64
proteins_100g                  64318 non-null float64
ingredients_from_palm_oil_n    53466 non-null float64
pnns_groups_2                  67038 non-null object
pnns_groups_1                  64881 non-null object
labels_tags                    35139 non-null object
countries_tags                 98440 non-null object
additives_tags                 30456 non-null object
additives_n                    53466 non-null float64
ingredients_text               53466 non-null object
image_url                      52540 non-null object
nutrition_scoring              61415 non-null float64
no_ingredients                 53466 non-null float64
no_ingredients_scoring         53466 non-null float64
additives_nocive_scoring       30456 non-null float64
energy_100g_scoring            64593 non-null float64
salt_100g_scoring              62574 non-null float64
sugars_100g_scoring            62515 non-null float64
saturated-fat_100g_scoring     62375 non-null float64
fiber_100g_scoring             45723 non-null float64
proteins_100g_scoring          64318 non-null float64
bio_scoring                    35139 non-null float64
dtypes: float64(19), int64(1), object(14)
memory usage: 25.5+ MB

scoring_features = ['nutrition_scoring', 'bio_scoring', 'no_ingredients_scoring',
       'additives_nocive_scoring', 'energy_100g_scoring', 'salt_100g_scoring',
       'sugars_100g_scoring', 'saturated-fat_100g_scoring',
       'fiber_100g_scoring', 'proteins_100g_scoring']

quantity_features = ['energy_100g', 'sugars_100g', 'salt_100g', 'saturated-fat_100g', 'fiber_100g', 'proteins_100g' ]

Anayse univariée des features de scoring¶

food.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 98440 entries, 0 to 98439
Data columns (total 34 columns):
code                           98440 non-null object
last_modified_t                98440 non-null int64
product_name                   91247 non-null object
states_tags                    98440 non-null object
main_category_fr               61955 non-null object
brands                         86426 non-null object
brands_tags                    86422 non-null object
nutrition_grade_fr             61415 non-null object
energy_100g                    64593 non-null float64
sugars_100g                    62515 non-null float64
salt_100g                      62574 non-null float64
saturated-fat_100g             62375 non-null float64
fiber_100g                     45723 non-null float64
proteins_100g                  64318 non-null float64
ingredients_from_palm_oil_n    53466 non-null float64
pnns_groups_2                  67038 non-null object
pnns_groups_1                  64881 non-null object
labels_tags                    35139 non-null object
countries_tags                 98440 non-null object
additives_tags                 30456 non-null object
additives_n                    53466 non-null float64
ingredients_text               53466 non-null object
image_url                      52540 non-null object
nutrition_scoring              61415 non-null float64
no_ingredients                 53466 non-null float64
no_ingredients_scoring         53466 non-null float64
additives_nocive_scoring       30456 non-null float64
energy_100g_scoring            64593 non-null float64
salt_100g_scoring              62574 non-null float64
sugars_100g_scoring            62515 non-null float64
saturated-fat_100g_scoring     62375 non-null float64
fiber_100g_scoring             45723 non-null float64
proteins_100g_scoring          64318 non-null float64
bio_scoring                    35139 non-null float64
dtypes: float64(19), int64(1), object(14)
memory usage: 25.5+ MB

food.describe()

food[scoring_features].hist(bins=5, figsize=(20,15))

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002468F1AED08>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F23AF88>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F276708>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002468F2AB288>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F2DAFC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F319B08>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002468F34FC08>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F386D48>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F391948>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002468F3C9B48>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F431048>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002468F46B248>]],
      dtype=object)

Focus sur les additifs, graisses, sucre, bio, et nombre d'ingrédients :¶

plt.style.use('default')
#print(plt.style.available)
#plt.style.use('seaborn-ticks')
#sns.set()
#current_palette = sns.color_palette('hls', 4)


for scoring_feature in ['additives_nocive_scoring', 'sugars_100g_scoring', 'saturated-fat_100g_scoring', 'bio_scoring', 'no_ingredients_scoring']:
        food.groupby(scoring_feature)[[scoring_feature]].count().plot.pie(subplots=True)
        #food.groupby(scoring_feature)[[scoring_feature]].count().plot.pie(subplots=True, colors=['#2c73d2', '#0081cf', '#0089ba', '#008e9b', '#008f7a'])

Constats, parmi les produits qui contiennent les informations nécessaire au scoring :¶

Près d'un quart des produits comportent au moins 1 additif considéré comme nocif
La moitié des produits comportent plus de 4,5g de sucre /100 (ils sont moins bien notés que le meilleur scoring de sucre)
Un quart des produits comportent plus de 7g de graisses saturées
Plus d'un quart des produits sont du bio français

Analyse univariée de features quantitatives¶

def log_convert(df, features_list_toconvert):
    features_list_converted = []
    for feature_name in features_list_toconvert:
        df[feature_name + '_log'] = np.log10(df[df[feature_name] > 0][feature_name])
        features_list_converted.append(feature_name + '_log')
        
    return(features_list_converted)

plt.rcParams["figure.figsize"] = [16,9]
food[quantity_features].hist(bins=50)
plt.suptitle("Analyse univariée des quantités\nEchelle abscisses : proportion en grammes / 100g \nEchelle ordonnées : nombre d'aliments")

Text(0.5, 0.98, "Analyse univariée des quantités\nEchelle abscisses : proportion en grammes / 100g \nEchelle ordonnées : nombre d'aliments")

Analyse univariée logarithmique, pour mieux voir les répartitions¶

features_list_log = log_convert(food, quantity_features)

features_list_log

['energy_100g_log',
 'sugars_100g_log',
 'salt_100g_log',
 'saturated-fat_100g_log',
 'fiber_100g_log',
 'proteins_100g_log']

food[features_list_log].hist(bins=50)
plt.suptitle("Analyse univariée logarithmique des quantités\nEchelle des abscisses logarithmique : -3=0.001, ..., 1 = 10, 2=100, 3=1000, ... en g / 100g\nEchelle des ordonnées : nombre d'aliments")

Text(0.5, 0.98, "Analyse univariée logarithmique des quantités\nEchelle des abscisses logarithmique : -3=0.001, ..., 1 = 10, 2=100, 3=1000, ... en g / 100g\nEchelle des ordonnées : nombre d'aliments")

food.describe()

Distribution des proportions d'énergie¶

plt.figure(figsize=(16, 10))
plt.axvline(np.log10(2345), 0, 1, color='red', label='à gauche de la barre rouge : scoring énergie > 1\nà droite de la barre rouge   : scoring énergie = 1')
plt.legend()
plt.title("Distribution des proportions d'énergie")
sns.distplot(food[food['energy_100g_log'].notnull()]['energy_100g_log'], kde=True, label='Densité de probabilité', axlabel='energie pour 100g (échelle logarithmique: 1 = 10, 2=100, 3=1000, ...)')
plt.legend()

<matplotlib.legend.Legend at 0x246951fa8c8>

Distribution des proportions de sel¶

plt.figure(figsize=(16, 10))
plt.axvline(np.log10(1.575), 0, 1, color='red', label='à gauche de la barre rouge : scoring sel > 1\nà droite de la barre rouge   : scoring sel = 1')
plt.legend()
plt.title('Distribution des proportions de sel')

sns.distplot(food[food['salt_100g_log'].notnull()]['salt_100g_log'], kde=True, label='Densité de probabilité', axlabel='sel pour 100g (échelle logarithmique: -3=0.001, ..., 1 = 10, 2=100, 3=1000, ...)')
plt.legend()

<matplotlib.legend.Legend at 0x2469abd2808>

Distribution des proportions de sucre¶

plt.figure(figsize=(16, 10))
plt.axvline(np.log10(31), 0, 1, color='red', label='à gauche de la barre rouge : scoring sucre > 1\nà droite de la barre rouge   : scoring sucre = 1')
plt.legend()
plt.title('Distribution des proportions de sucre')
sns.distplot(food[food['sugars_100g_log'].notnull()]['sugars_100g_log'], kde=True, label='Densité de probabilité', axlabel='sucre pour 100g (échelle logarithmique: -3=0.001, ..., 1 = 10, 2=100, 3=1000, ...)')
plt.legend()

<matplotlib.legend.Legend at 0x2469bffdc48>

Analyse multivariée : Corrélation entre les données¶

corr_matrix = food.corr()

corr_matrix[quantity_features].loc[quantity_features]

plt.title('Corrélation entre les proportions')
sns.heatmap(corr_matrix[quantity_features].loc[quantity_features], 
        xticklabels=corr_matrix[quantity_features].loc[quantity_features].columns,
        yticklabels=corr_matrix[quantity_features].loc[quantity_features].columns, cmap='coolwarm' ,center=0.20)

<matplotlib.axes._subplots.AxesSubplot at 0x2469b9fa748>

corr_matrix[scoring_features].loc[scoring_features]

plt.title('Corrélation entre les scorings de qualité nutritionnelle')
sns.heatmap(corr_matrix[scoring_features].loc[scoring_features], 
        xticklabels=corr_matrix[scoring_features].loc[scoring_features].columns,
        yticklabels=corr_matrix[scoring_features].loc[scoring_features].columns, cmap='coolwarm', center=0.20)

<matplotlib.axes._subplots.AxesSubplot at 0x2469bf65c08>

On voit que le scoring nutrition est bien corrélé aux variables sur lesquels porte ce scoring : graisses, sucres, énergie, sel¶

no_ingredients et bio sont également corrélés aux autres variables (couleur bleu clair)¶

Test khi 2 entre le scoring de nutrition et bio¶

X = "bio_scoring"
Y = "nutrition_scoring"
data = food

cont = data[[X,Y]].pivot_table(index=X,columns=Y,aggfunc=len,margins=True,margins_name="Total")
cont

tx = cont.loc[:,["Total"]]
ty = cont.loc[["Total"],:]
n = len(data)
indep = tx.dot(ty) / n

c = cont.fillna(0) # On remplace les valeurs nulles par 0
measure = (c-indep)**2/indep
xi_n = measure.sum().sum()
table = measure/xi_n

sns.heatmap(table.iloc[:-1,:-1],annot=c.iloc[:-1,:-1], linewidths=.5)
plt.show()

Diagramme de dispersion des quantités¶

scatter_matrix(food[features_list_log], figsize=(16,16))
plt.suptitle('Diagramme de dispersion des quantités')

Text(0.5, 0.98, 'Diagramme de dispersion des quantités')

Réduction dimensionnelle¶

from sklearn import decomposition
from sklearn import preprocessing

# Import `PCA` from `sklearn.decomposition`
from sklearn.decomposition import PCA

# Build the model
pca = PCA(n_components=2)

# import de l'échantillon
data = food

# selection des colonnes à prendre en compte dans l'ACP
data_pca = food[['nutrition_scoring', 'no_ingredients_scoring',
       'additives_nocive_scoring', 'energy_100g_scoring', 'salt_100g_scoring',
       'sugars_100g_scoring', 'saturated-fat_100g_scoring',
       'fiber_100g_scoring', 'proteins_100g_scoring', 'bio_scoring']]

data_pca = data_pca.dropna()

X = data_pca.values
features = data_pca.columns

# Centrage et Réduction
std_scale = preprocessing.StandardScaler().fit(X)
X_scaled = std_scale.transform(X)

# Reduce the data, output is ndarray
reduced_data = pca.fit_transform(X_scaled)

# Inspect shape of the `reduced_data`
print(reduced_data.shape)

# print out the reduced data
print(reduced_data)

(8157, 2)
[[ 1.09517571  0.84077433]
 [ 0.75214046  0.09624672]
 [ 2.69990706  0.96754955]
 ...
 [ 1.24321778  0.1969745 ]
 [-1.45885812  0.40773526]
 [-1.64721864  0.15928317]]

Représentation des produits en 2 dimensions avec coloration nutrition score¶

Cercle des corrélations et réduction de dimensionalité¶

import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import dendrogram

def display_circles(pcs, n_comp, pca, axis_ranks, labels=None, label_rotation=0, lims=None):
    for d1, d2 in axis_ranks: # On affiche les 3 premiers plans factoriels, donc les 6 premières composantes
        if d2 < n_comp:

            # initialisation de la figure
            fig, ax = plt.subplots(figsize=(16,16))

            # détermination des limites du graphique
            if lims is not None :
                xmin, xmax, ymin, ymax = lims
            elif pcs.shape[1] < 30 :
                xmin, xmax, ymin, ymax = -1, 1, -1, 1
            else :
                xmin, xmax, ymin, ymax = min(pcs[d1,:]), max(pcs[d1,:]), min(pcs[d2,:]), max(pcs[d2,:])

            # affichage des flèches
            # s'il y a plus de 30 flèches, on n'affiche pas le triangle à leur extrémité
            if pcs.shape[1] < 30 :
                plt.quiver(np.zeros(pcs.shape[1]), np.zeros(pcs.shape[1]),
                   pcs[d1,:], pcs[d2,:], 
                   angles='xy', scale_units='xy', scale=1, color="grey")
                # (voir la doc : https://matplotlib.org/api/_as_gen/matplotlib.pyplot.quiver.html)
            else:
                lines = [[[0,0],[x,y]] for x,y in pcs[[d1,d2]].T]
                ax.add_collection(LineCollection(lines, axes=ax, alpha=.1, color='black'))
            
            # affichage des noms des variables  
            if labels is not None:  
                for i,(x, y) in enumerate(pcs[[d1,d2]].T):
                    if x >= xmin and x <= xmax and y >= ymin and y <= ymax :
                        plt.text(x, y, labels[i], fontsize='14', ha='center', va='center', rotation=label_rotation, color="blue", alpha=0.5)
            
            # affichage du cercle
            circle = plt.Circle((0,0), 1, facecolor='none', edgecolor='b')
            plt.gca().add_artist(circle)

            # définition des limites du graphique
            plt.xlim(xmin, xmax)
            plt.ylim(ymin, ymax)

        
            # affichage des lignes horizontales et verticales
            plt.plot([-1, 1], [0, 0], color='grey', ls='--')
            plt.plot([0, 0], [-1, 1], color='grey', ls='--')

            # nom des axes, avec le pourcentage d'inertie expliqué
            plt.xlabel('F{} ({}%)'.format(d1+1, round(100*pca.explained_variance_ratio_[d1],1)))
            plt.ylabel('F{} ({}%)'.format(d2+1, round(100*pca.explained_variance_ratio_[d2],1)))

            plt.title("Cercle des corrélations (F{} et F{})".format(d1+1, d2+1))
            plt.show(block=False)
        
def display_factorial_planes(X_projected, n_comp, pca, axis_ranks, labels=None, alpha=1, illustrative_var=None):
    for d1,d2 in axis_ranks:
        if d2 < n_comp:
 
            # initialisation de la figure       
            fig = plt.figure(figsize=(10,6))
        
            # affichage des points
            if illustrative_var is None:
                plt.scatter(X_projected[:, d1], X_projected[:, d2], alpha=alpha)
            else:
                illustrative_var = np.array(illustrative_var)
                for value in np.unique(illustrative_var):
                    selected = np.where(illustrative_var == value)
                    plt.scatter(X_projected[selected, d1], X_projected[selected, d2], alpha=alpha, label=value)
                plt.legend()

            # affichage des labels des points
            if labels is not None:
                for i,(x,y) in enumerate(X_projected[:,[d1,d2]]):
                    plt.text(x, y, labels[i],
                              fontsize='14', ha='center',va='center') 
                
            # détermination des limites du graphique
            boundary = np.max(np.abs(X_projected[:, [d1,d2]])) * 1.1
            plt.xlim([-boundary,boundary])
            plt.ylim([-boundary,boundary])
        
            # affichage des lignes horizontales et verticales
            plt.plot([-100, 100], [0, 0], color='grey', ls='--')
            plt.plot([0, 0], [-100, 100], color='grey', ls='--')

            # nom des axes, avec le pourcentage d'inertie expliqué
            plt.xlabel('F{} ({}%)'.format(d1+1, round(100*pca.explained_variance_ratio_[d1],1)))
            plt.ylabel('F{} ({}%)'.format(d2+1, round(100*pca.explained_variance_ratio_[d2],1)))

            plt.title("Projection des aliments (coloration : nutrition score) (sur F{} et F{})".format(d1+1, d2+1))
            plt.show(block=False)

def display_scree_plot(pca):
    scree = pca.explained_variance_ratio_*100
    plt.bar(np.arange(len(scree))+1, scree)
    plt.plot(np.arange(len(scree))+1, scree.cumsum(),c="red",marker='o')
    plt.xlabel("rang de l'axe d'inertie")
    plt.ylabel("pourcentage d'inertie")
    plt.title("Eboulis des valeurs propres")
    plt.show(block=False)

def plot_dendrogram(Z, names):
    plt.figure(figsize=(10,25))
    plt.title('Hierarchical Clustering Dendrogram')
    plt.xlabel('distance')
    dendrogram(
        Z,
        labels = names,
        orientation = "left",
    )
plt.show()

from sklearn import decomposition
from sklearn import preprocessing

# Import `PCA` from `sklearn.decomposition`
from sklearn.decomposition import PCA

# Build the model
pca = PCA(n_components=2)

# choix du nombre de composantes à calculer
n_comp = 6

# import de l'échantillon
data = food

# selection des colonnes à prendre en compte dans l'ACP
data_pca = food[['nutrition_scoring', 'no_ingredients_scoring',
       'additives_nocive_scoring', 'energy_100g_scoring', 'salt_100g_scoring',
       'sugars_100g_scoring', 'saturated-fat_100g_scoring',
       'fiber_100g_scoring', 'proteins_100g_scoring', 'bio_scoring']]

# préparation des données pour l'ACP
#data_pca = data_pca.fillna(data_pca.mean()) # Il est fréquent de remplacer les valeurs inconnues par la moyenne de la variable
data_pca = data_pca.dropna()

X = data_pca.values
#names = data["idCours"] # ou data.index pour avoir les intitulés

#features = data.columns
features = data_pca.columns

# Centrage et Réduction
std_scale = preprocessing.StandardScaler().fit(X)
X_scaled = std_scale.transform(X)

# Calcul des composantes principales
pca = decomposition.PCA(n_components=n_comp)
pca.fit(X_scaled)

# Eboulis des valeurs propres
display_scree_plot(pca)

# Cercle des corrélations
pcs = pca.components_
#plt.figure(figsize=(16,10))
plt.rcParams["figure.figsize"] = [16,9]
display_circles(pcs, n_comp, pca, [(0,1),(2,3),(4,5)], labels = np.array(features))


# Projection des individus
X_projected = pca.transform(X_scaled)
display_factorial_planes(X_projected, n_comp, pca, [(0,1),(2,3),(4,5)], illustrative_var=data_pca[['nutrition_scoring']].values[:,0])


plt.show()

pcs[0]

array([-0.43722506, -0.17992925,  0.03420172, -0.52448591, -0.11230503,
       -0.32460514, -0.47118322,  0.26501861,  0.29681772,  0.04329341])

data_pca

Analyse d'échantillons d'ingrédients obtenus après application des scorings¶

On définit un nouveau dataframe qui contiendra uniquement les features de scoring les plus importantes, et qui n'ont pas de valeurs NA :¶

Les features de scoring sur les proportions/100g ont été enlevées car elles sont couvertes par le nutrition score

food_scoring_important = food[['code', 'product_name', 'image_url', 'main_category_fr', 'nutrition_scoring', 'no_ingredients_scoring', 'additives_nocive_scoring', 'bio_scoring']].dropna()

food_scoring_important.shape

(10136, 8)

Si on ne conserve que les aliments ayant tous les scoring nutrition, additifs, bio, nombre d'ingrédients, supérieurs à 5 :¶

food_scoring_important = food[(food['nutrition_scoring'] == 5) & (food['additives_nocive_scoring'] == 5) & (food['bio_scoring'] == 5) & (food['no_ingredients_scoring'] == 5)]

Catégorie d'aliments à proposer dans ce cas :¶

food_scoring_important['main_category_fr'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x2469cf29948>

=> La variété des ingrédients disponibles est un peu faible¶

Si on ne conserve que les aliments ayant nutrition scoring à 5 et bio scoring à 5, sans tenir compte du scoring sur le nombre d'ingrédients :¶

NB : puisque le scoring bio à 5 implique dene pas avoir d'additif, on n'inclut pas les additifs nocifs dans ce cas

food_scoring_important = food[(food_scoring_important['nutrition_scoring'] == 5) &  (food['bio_scoring'] == 5)]

Catégorie d'aliments à proposer dans ce cas :¶

food_scoring_important['main_category_fr'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x246a0a79b88>

=> La variété des ingrédients disponibles reste faible¶

Si on ne conserve que les aliments ayant nutrition scoring >= 5, avec un scoring bio >= 2 (ce qui signifie au minimum des produits français, qu'ils soient bio ou non), et un scoring sur le nombre d'ingrédients >= 2 (c'est à dire pas plus de 5 ingrédients pour éviter les produits transformés) :¶

food_scoring_important = food[(food['nutrition_scoring'] >= 5) & (food['bio_scoring'] >= 2)& (food['no_ingredients_scoring'] >= 4)]

Catégorie d'aliments à proposer dans ce cas :¶

food_scoring_important['main_category_fr'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x2469d324388>

food_scoring_important['main_category_fr'].value_counts()[:30]

Aliments et boissons à base de végétaux    216
Pâtes alimentaires                         176
Desserts                                   110
Conserves                                   94
Pains                                       54
Riz                                         44
Petit-déjeuners                             37
Surgelés                                    36
Légumes frais                               31
Farines de blé                              31
Viandes                                     26
Farines                                     24
Laits                                       22
Produits labellisés                         21
Coquillettes                                19
Fromages blancs                             18
Yaourts                                     16
Boissons                                    14
Produits d'élevages                         13
Compotes pour bébé                          11
Epicerie                                    11
Aliments pour bébé                          10
Volailles                                    8
Fruits                                       7
Plats préparés                               7
Fruits secs                                  6
Pates                                        5
Chips et frites                              5
Crustacés                                    5
Fruits à coques                              4
Name: main_category_fr, dtype: int64

=> La variété des ingrédients disponibles est satisfaisante¶

Affichage de fiches scoring des bons ingrédients, avec images¶

import plotly.express as px

#from IPython.display import HTML, display
from IPython.display import Image

'''
Cette fonction nécessite une variable globale "scoring_features"
qui contient la liste des noms de colonnes du dataframe df à afficher dans le radar plot
'''
def display_products_radar_image(df):
    max_products_display = 100
    cnt = 0
    
    for i, j in df.iterrows(): 
        if (cnt > max_products_display):
            print('Max products display reached')
            break
            
        radar_values = df.loc[[i]][scoring_features].to_numpy()
        #print(radar_values.tolist()[0])
        radar_values[np.isnan(radar_values)] = 0
        #print(radar_values.tolist()[0])
        
        df_radius = pd.DataFrame(dict(
            r = radar_values.tolist()[0],
            theta = scoring_features))

        fig = px.line_polar(df_radius, r='r', theta='theta', line_close=True, width=600, height=400, title=df.loc[i]['product_name'])
        
        plt.figure(figsize=(10, 10))
        
        print('Produit: ')
        fig.show()
                
        #print('Image du produit: ')  
        image_url = df.loc[i]['image_url']
                        
        if (type(image_url) == str):
            print(f'image_url = <<{image_url}>>')
            display(Image(df.loc[i]['image_url'], width=100))
                        
        print('\n\n')
        cnt +=1

food_scoring_important[food_scoring_important['main_category_fr'] == 'Viandes']

display_products_radar_image(food_scoring_important[food_scoring_important['main_category_fr'] == 'Viandes'].head(20))

Produit:



Produit:



Produit:

image_url = <<http://fr.openfoodfacts.org/images/products/029/245/202/4530/front.6.400.jpg>>

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1271         try:
-> 1272             b64_data = b2a_base64(self.data).decode('ascii')
   1273         except TypeError:

TypeError: a bytes-like object is required, not 'NoneType'

During handling of the above exception, another exception occurred:

FileNotFoundError                         Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\formatters.py in __call__(self, obj, include, exclude)
    968 
    969             if method is not None:
--> 970                 return method(include=include, exclude=exclude)
    971             return None
    972         else:

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _repr_mimebundle_(self, include, exclude)
   1260         if self.embed:
   1261             mimetype = self._mimetype
-> 1262             data, metadata = self._data_and_metadata(always_both=True)
   1263             if metadata:
   1264                 metadata = {mimetype: metadata}

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1273         except TypeError:
   1274             raise FileNotFoundError(
-> 1275                 "No such file or directory: '%s'" % (self.data))
   1276         md = {}
   1277         if self.metadata:

FileNotFoundError: No such file or directory: 'None'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1271         try:
-> 1272             b64_data = b2a_base64(self.data).decode('ascii')
   1273         except TypeError:

TypeError: a bytes-like object is required, not 'NoneType'

During handling of the above exception, another exception occurred:

FileNotFoundError                         Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    343             method = get_real_method(obj, self.print_method)
    344             if method is not None:
--> 345                 return method()
    346             return None
    347         else:

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _repr_jpeg_(self)
   1294     def _repr_jpeg_(self):
   1295         if self.embed and self.format == self._FMT_JPEG:
-> 1296             return self._data_and_metadata()
   1297 
   1298     def _find_ext(self, s):

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1273         except TypeError:
   1274             raise FileNotFoundError(
-> 1275                 "No such file or directory: '%s'" % (self.data))
   1276         md = {}
   1277         if self.metadata:

FileNotFoundError: No such file or directory: 'None'

<IPython.core.display.Image object>



Produit:



Produit:

image_url = <<http://fr.openfoodfacts.org/images/products/029/247/102/0476/front.7.400.jpg>>

for cat_name in food_scoring_important['main_category_fr'].value_counts()[:10].iteritems():
    print(f'5 bons produits dans la catégorie {cat_name[0]}')
    
    display_products_radar_image(food_scoring_important[food_scoring_important['main_category_fr'] == cat_name[0]].head(5))

5 bons produits dans la catégorie Aliments et boissons à base de végétaux
Produit:



Produit:

image_url = <<http://fr.openfoodfacts.org/images/products/22049896/front.7.400.jpg>>

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1271         try:
-> 1272             b64_data = b2a_base64(self.data).decode('ascii')
   1273         except TypeError:

TypeError: a bytes-like object is required, not 'NoneType'

During handling of the above exception, another exception occurred:

FileNotFoundError                         Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\formatters.py in __call__(self, obj, include, exclude)
    968 
    969             if method is not None:
--> 970                 return method(include=include, exclude=exclude)
    971             return None
    972         else:

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _repr_mimebundle_(self, include, exclude)
   1260         if self.embed:
   1261             mimetype = self._mimetype
-> 1262             data, metadata = self._data_and_metadata(always_both=True)
   1263             if metadata:
   1264                 metadata = {mimetype: metadata}

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1273         except TypeError:
   1274             raise FileNotFoundError(
-> 1275                 "No such file or directory: '%s'" % (self.data))
   1276         md = {}
   1277         if self.metadata:

FileNotFoundError: No such file or directory: 'None'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1271         try:
-> 1272             b64_data = b2a_base64(self.data).decode('ascii')
   1273         except TypeError:

TypeError: a bytes-like object is required, not 'NoneType'

During handling of the above exception, another exception occurred:

FileNotFoundError                         Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    343             method = get_real_method(obj, self.print_method)
    344             if method is not None:
--> 345                 return method()
    346             return None
    347         else:

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _repr_jpeg_(self)
   1294     def _repr_jpeg_(self):
   1295         if self.embed and self.format == self._FMT_JPEG:
-> 1296             return self._data_and_metadata()
   1297 
   1298     def _find_ext(self, s):

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1273         except TypeError:
   1274             raise FileNotFoundError(
-> 1275                 "No such file or directory: '%s'" % (self.data))
   1276         md = {}
   1277         if self.metadata:

FileNotFoundError: No such file or directory: 'None'

<IPython.core.display.Image object>



Produit:

image_url = <<http://fr.openfoodfacts.org/images/products/222/981/618/9381/front.3.400.jpg>>

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1271         try:
-> 1272             b64_data = b2a_base64(self.data).decode('ascii')
   1273         except TypeError:

TypeError: a bytes-like object is required, not 'NoneType'

During handling of the above exception, another exception occurred:

FileNotFoundError                         Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\formatters.py in __call__(self, obj, include, exclude)
    968 
    969             if method is not None:
--> 970                 return method(include=include, exclude=exclude)
    971             return None
    972         else:

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _repr_mimebundle_(self, include, exclude)
   1260         if self.embed:
   1261             mimetype = self._mimetype
-> 1262             data, metadata = self._data_and_metadata(always_both=True)
   1263             if metadata:
   1264                 metadata = {mimetype: metadata}

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1273         except TypeError:
   1274             raise FileNotFoundError(
-> 1275                 "No such file or directory: '%s'" % (self.data))
   1276         md = {}
   1277         if self.metadata:

FileNotFoundError: No such file or directory: 'None'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1271         try:
-> 1272             b64_data = b2a_base64(self.data).decode('ascii')
   1273         except TypeError:

TypeError: a bytes-like object is required, not 'NoneType'

During handling of the above exception, another exception occurred:

FileNotFoundError                         Traceback (most recent call last)
~\.conda\envs\FBO\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    343             method = get_real_method(obj, self.print_method)
    344             if method is not None:
--> 345                 return method()
    346             return None
    347         else:

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _repr_jpeg_(self)
   1294     def _repr_jpeg_(self):
   1295         if self.embed and self.format == self._FMT_JPEG:
-> 1296             return self._data_and_metadata()
   1297 
   1298     def _find_ext(self, s):

~\.conda\envs\FBO\lib\site-packages\IPython\core\display.py in _data_and_metadata(self, always_both)
   1273         except TypeError:
   1274             raise FileNotFoundError(
-> 1275                 "No such file or directory: '%s'" % (self.data))
   1276         md = {}
   1277         if self.metadata:

FileNotFoundError: No such file or directory: 'None'

Annexe : essai du composant qgrid¶

import qgrid
qgrid.show_grid(food, grid_options={'forceFitColumns': False, 'defaultColumnWidth': 150})

	code	last_modified_t	product_name	states_tags	main_category_fr	brands	brands_tags	nutrition_grade_fr	energy_100g	sugars_100g	salt_100g	saturated-fat_100g	fiber_100g	proteins_100g	ingredients_from_palm_oil_n	pnns_groups_2	pnns_groups_1	labels_tags	countries_tags	additives_tags	additives_n	ingredients_text	image_url	nutrition_scoring	no_ingredients	no_ingredients_scoring	additives_nocive_scoring	energy_100g_scoring	salt_100g_scoring	sugars_100g_scoring	saturated-fat_100g_scoring	fiber_100g_scoring	proteins_100g_scoring	bio_scoring
0	0000000003087	1474103893	Farine de blé noir	en:to-be-completed,en:nutrition-facts-to-be-co...	NaN	Ferme t'y R'nao	ferme-t-y-r-nao	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	en:france	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	0000000024600	1434535914	Filet de bœuf	en:to-be-completed,en:nutrition-facts-to-be-co...	Filet-de-boeuf	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	unknown	unknown	NaN	en:france	NaN	NaN	NaN	http://fr.openfoodfacts.org/images/products/00...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	0000000027205	1458238638	NaN	en:to-be-completed,en:nutrition-facts-to-be-co...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	en:france	NaN	NaN	NaN	http://fr.openfoodfacts.org/images/products/00...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	0000000036252	1489055667	Lion Peanut x2	en:to-be-completed,en:nutrition-facts-complete...	NaN	Sunridge	sunridge	e	1883.0	57.5	0.09652	12.5	2.5	2.5	0.0	unknown	unknown	NaN	en:france,en:united-states	en:e322	1.0	Milk chocolate coating (evaporated cane juice,...	http://fr.openfoodfacts.org/images/products/00...	1.0	10.0	2.0	5.0	2.0	5.0	1.0	1.0	2.0	1.0	NaN
4	0000000039259	1473538082	Twix x2	en:to-be-completed,en:nutrition-facts-to-be-co...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	unknown	unknown	NaN	en:france	NaN	NaN	NaN	http://fr.openfoodfacts.org/images/products/00...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	last_modified_t	energy_100g	sugars_100g	salt_100g	saturated-fat_100g	fiber_100g	proteins_100g	ingredients_from_palm_oil_n	additives_n	nutrition_scoring	no_ingredients	no_ingredients_scoring	additives_nocive_scoring	energy_100g_scoring	salt_100g_scoring	sugars_100g_scoring	saturated-fat_100g_scoring	fiber_100g_scoring	proteins_100g_scoring	bio_scoring
count	9.844000e+04	6.459300e+04	62515.000000	62574.000000	62375.000000	45723.000000	64318.000000	53466.000000	53466.000000	61415.000000	53466.000000	53466.000000	30456.000000	64593.000000	62574.000000	62515.000000	62375.000000	45723.000000	64318.000000	35139.000000
mean	1.468599e+09	1.171553e+03	13.432792	1.160535	5.423696	2.559271	7.754531	0.069839	1.856619	2.820874	10.421632	2.815696	4.054768	3.393820	3.485170	3.854115	3.431086	2.074230	3.051012	2.346766
std	2.574637e+07	1.283590e+04	19.087618	4.309815	8.531083	4.634788	7.887373	0.258593	2.561405	1.352756	10.191214	1.690744	1.699282	1.155246	1.510884	1.493550	1.650643	1.512065	1.711937	1.776803
min	1.333873e+09	0.000000e+00	-0.100000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
25%	1.453544e+09	4.270000e+02	1.000000	0.080000	0.300000	0.000000	1.800000	0.000000	0.000000	2.000000	3.000000	1.000000	5.000000	3.000000	2.000000	3.000000	1.000000	1.000000	1.000000	1.000000
50%	1.477596e+09	1.035000e+03	4.100000	0.558800	2.000000	1.380000	6.000000	0.000000	1.000000	3.000000	7.000000	3.000000	5.000000	3.000000	4.000000	5.000000	4.000000	1.000000	3.000000	1.000000
75%	1.488821e+09	1.649000e+03	17.800000	1.244600	7.400000	3.200000	11.000000	0.000000	3.000000	4.000000	15.000000	5.000000	5.000000	4.000000	5.000000	5.000000	5.000000	3.000000	5.000000	5.000000
max	1.492736e+09	3.251373e+06	105.000000	211.000000	210.000000	178.000000	100.000000	2.000000	31.000000	5.000000	138.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000

	last_modified_t	energy_100g	sugars_100g	salt_100g	saturated-fat_100g	fiber_100g	proteins_100g	ingredients_from_palm_oil_n	additives_n	nutrition_scoring	no_ingredients	no_ingredients_scoring	additives_nocive_scoring	energy_100g_scoring	salt_100g_scoring	sugars_100g_scoring	saturated-fat_100g_scoring	fiber_100g_scoring	proteins_100g_scoring	bio_scoring	energy_100g_log	sugars_100g_log	salt_100g_log	saturated-fat_100g_log	fiber_100g_log	proteins_100g_log
count	9.844000e+04	6.459300e+04	62515.000000	62574.000000	62375.000000	45723.000000	64318.000000	53466.000000	53466.000000	61415.000000	53466.000000	53466.000000	30456.000000	64593.000000	62574.000000	62515.000000	62375.000000	45723.000000	64318.000000	35139.000000	64256.000000	58236.000000	57182.000000	54691.000000	32028.000000	60543.000000
mean	1.468599e+09	1.171553e+03	13.432792	1.160535	5.423696	2.559271	7.754531	0.069839	1.856619	2.820874	10.421632	2.815696	4.054768	3.393820	3.485170	3.854115	3.431086	2.074230	3.051012	2.346766	2.891262	0.699488	-0.424255	0.336312	0.325363	0.670298
std	2.574637e+07	1.283590e+04	19.087618	4.309815	8.531083	4.634788	7.887373	0.258593	2.561405	1.352756	10.191214	1.690744	1.699282	1.155246	1.510884	1.493550	1.650643	1.512065	1.711937	1.776803	0.451343	0.719159	0.776010	0.740458	0.494054	0.560380
min	1.333873e+09	0.000000e+00	-0.100000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.376751	-4.000000	-7.301030	-4.000000	-4.000000	-4.000000
25%	1.453544e+09	4.270000e+02	1.000000	0.080000	0.300000	0.000000	1.800000	0.000000	0.000000	2.000000	3.000000	1.000000	5.000000	3.000000	2.000000	3.000000	1.000000	1.000000	1.000000	1.000000	2.638489	0.176091	-0.958607	-0.154902	0.079181	0.431364
50%	1.477596e+09	1.035000e+03	4.100000	0.558800	2.000000	1.380000	6.000000	0.000000	1.000000	3.000000	7.000000	3.000000	5.000000	3.000000	4.000000	5.000000	4.000000	1.000000	3.000000	1.000000	3.017033	0.681241	-0.167491	0.414973	0.369212	0.799341
75%	1.488821e+09	1.649000e+03	17.800000	1.244600	7.400000	3.200000	11.000000	0.000000	3.000000	4.000000	15.000000	5.000000	5.000000	4.000000	5.000000	5.000000	5.000000	3.000000	5.000000	5.000000	3.218273	1.307496	0.113943	0.949390	0.633468	1.045323
max	1.492736e+09	3.251373e+06	105.000000	211.000000	210.000000	178.000000	100.000000	2.000000	31.000000	5.000000	138.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	6.512067	2.021189	2.324282	2.322219	2.250420	2.000000

	energy_100g	sugars_100g	salt_100g	saturated-fat_100g	fiber_100g	proteins_100g
energy_100g	1.000000	0.017048	-0.002525	0.031784	0.023485	0.019534
sugars_100g	0.017048	1.000000	-0.085952	0.080119	0.036147	-0.245816
salt_100g	-0.002525	-0.085952	1.000000	0.008326	0.003046	0.092610
saturated-fat_100g	0.031784	0.080119	0.008326	1.000000	0.019374	0.143507
fiber_100g	0.023485	0.036147	0.003046	0.019374	1.000000	0.197523
proteins_100g	0.019534	-0.245816	0.092610	0.143507	0.197523	1.000000

	nutrition_scoring	bio_scoring	no_ingredients_scoring	additives_nocive_scoring	energy_100g_scoring	salt_100g_scoring	sugars_100g_scoring	saturated-fat_100g_scoring	fiber_100g_scoring	proteins_100g_scoring
nutrition_scoring	1.000000	0.123408	0.226102	0.015085	0.529364	0.255817	0.439241	0.659987	0.167601	-0.059106
bio_scoring	0.123408	1.000000	0.165324	0.117165	-0.055108	0.207577	-0.054466	0.079399	0.170695	-0.026112
no_ingredients_scoring	0.226102	0.165324	1.000000	0.130712	0.099992	0.259121	0.158221	0.165564	0.043054	-0.057208
additives_nocive_scoring	0.015085	0.117165	0.130712	1.000000	-0.081270	0.278058	-0.242503	-0.045346	0.099225	-0.217596
energy_100g_scoring	0.529364	-0.055108	0.099992	-0.081270	1.000000	0.123336	0.361267	0.631874	-0.351808	-0.384084
salt_100g_scoring	0.255817	0.207577	0.259121	0.278058	0.123336	1.000000	-0.349230	0.196985	0.019582	-0.412878
sugars_100g_scoring	0.439241	-0.054466	0.158221	-0.242503	0.361267	-0.349230	1.000000	0.186794	-0.116794	0.170938
saturated-fat_100g_scoring	0.659987	0.079399	0.165564	-0.045346	0.631874	0.196985	0.186794	1.000000	-0.026331	-0.317821
fiber_100g_scoring	0.167601	0.170695	0.043054	0.099225	-0.351808	0.019582	-0.116794	-0.026331	1.000000	0.323441
proteins_100g_scoring	-0.059106	-0.026112	-0.057208	-0.217596	-0.384084	-0.412878	0.170938	-0.317821	0.323441	1.000000

nutrition_scoring	1.0	2.0	3.0	4.0	5.0	Total
bio_scoring
1.0	2708	4330	3483	2391	2725	15637
2.0	440	658	780	470	316	2664
3.0	16	45	32	24	51	168
4.0	119	229	132	110	181	771
5.0	1075	1816	1576	1487	2287	8241
Total	4358	7078	6003	4482	5560	27481

	nutrition_scoring	no_ingredients_scoring	additives_nocive_scoring	energy_100g_scoring	salt_100g_scoring	sugars_100g_scoring	saturated-fat_100g_scoring	fiber_100g_scoring	proteins_100g_scoring	bio_scoring
24	2.0	1.0	5.0	2.0	4.0	2.0	4.0	1.0	3.0	2.0
26	2.0	1.0	1.0	3.0	4.0	1.0	4.0	1.0	3.0	2.0
28	1.0	1.0	5.0	2.0	4.0	1.0	1.0	1.0	4.0	2.0
29	1.0	1.0	1.0	2.0	3.0	1.0	1.0	2.0	5.0	2.0
54	2.0	3.0	5.0	3.0	5.0	1.0	5.0	1.0	1.0	1.0
...	...	...	...	...	...	...	...	...	...	...
98354	2.0	2.0	5.0	5.0	1.0	4.0	5.0	1.0	2.0	1.0
98360	1.0	1.0	5.0	3.0	1.0	1.0	5.0	1.0	1.0	1.0
98375	3.0	3.0	5.0	3.0	3.0	4.0	1.0	5.0	3.0	1.0
98395	3.0	5.0	1.0	3.0	5.0	5.0	4.0	1.0	1.0	1.0
98405	4.0	4.0	1.0	3.0	5.0	5.0	4.0	1.0	1.0	1.0

	code	last_modified_t	product_name	states_tags	main_category_fr	brands	brands_tags	nutrition_grade_fr	energy_100g	sugars_100g	salt_100g	saturated-fat_100g	fiber_100g	proteins_100g	pnns_groups_2	pnns_groups_1	labels_tags	countries_tags	additives_tags	additives_n	ingredients_text	image_url	nutrition_scoring	no_ingredients	no_ingredients_scoring	additives_nocive_scoring	energy_100g_scoring	salt_100g_scoring	sugars_100g_scoring	saturated-fat_100g_scoring	fiber_100g_scoring	proteins_100g_scoring	bio_scoring	energy_100g_log	sugars_100g_log	salt_100g_log	saturated-fat_100g_log	fiber_100g_log	proteins_100g_log
2373	0291562071304	1486242392	Filet mignon de porc extra tendre fumé délicat...	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Cooperl	cooperl	a	590.0	0.2	0.460000	1.6	NaN	25.8	Meat	Fish Meat Eggs	fr:viande-francaise,en:produced-in-bretagne,fr...	en:france	NaN	0.0	Filet mignon de porc	NaN	5.0	1.0	5.0	NaN	4.0	4.0	5.0	4.0	NaN	5.0	2.0	2.770852	-0.69897	-0.337242	0.204120	NaN	1.411620
2389	0292451081671	1476301667	2 Tournedos dans le Filet	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	476.0	0.0	0.170000	0.8	NaN	23.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,fr:origine-fr...	en:france	NaN	0.0	Viande bovine, barde de porc naturelle.	NaN	5.0	2.0	5.0	NaN	4.0	5.0	5.0	5.0	NaN	5.0	2.0	2.677607	NaN	-0.769551	-0.096910	NaN	1.361728
2393	0292452024530	1427217137	1 Faux-Filet persillé & savoureux	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	633.0	0.0	0.200000	3.4	NaN	22.0	Meat	Fish Meat Eggs	fr:viande-francaise,fr:viande-bovine-francaise	en:france	NaN	0.0	Faux filet de boeuf ***	http://fr.openfoodfacts.org/images/products/02...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	3.0	NaN	5.0	2.0	2.801404	NaN	-0.698970	0.531479	NaN	1.342423
2395	0292471016998	1475525234	Bifteck	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	476.0	0.0	0.170000	0.8	NaN	23.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,en:made-in-fr...	en:france	NaN	0.0	Steak ***	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	5.0	NaN	5.0	2.0	2.677607	NaN	-0.769551	-0.096910	NaN	1.361728
2397	0292471020476	1475524445	1 Bifteck	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	465.0	0.0	0.200000	0.9	NaN	23.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,fr:viande-bov...	en:france	NaN	0.0	Steak ***	http://fr.openfoodfacts.org/images/products/02...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	5.0	NaN	5.0	2.0	2.667453	NaN	-0.698970	-0.045757	NaN	1.361728
2402	0292472040343	1478893392	2 Biftecks	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	476.0	0.0	0.170000	0.8	NaN	23.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,en:made-in-fr...	en:france	NaN	0.0	Viande bovine fraîche protégée sous vide.	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	5.0	NaN	5.0	2.0	2.677607	NaN	-0.769551	-0.096910	NaN	1.361728
2406	0292475048346	1477213225	2 Bavettes d'Aloyau	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	551.0	0.0	0.190000	2.2	NaN	20.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,en:made-in-fr...	en:france	NaN	0.0	Viande bovine 100%	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.741152	NaN	-0.721246	0.342423	NaN	1.301030
2408	0292477060544	1472663780	2 Faux-Filets de Bœuf	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	622.0	0.0	0.160000	1.7	NaN	22.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,fr:viande-bov...	en:france	NaN	0.0	100% viande de bœuf	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.793790	NaN	-0.795880	0.230449	NaN	1.342423
6944	20638610	1488970898	Rôti** à rôtir	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	L'étal du boucher	l-etal-du-boucher	a	487.0	0.0	0.150000	0.8	NaN	20.6	Meat	Fish Meat Eggs	fr:viande-francaise,fr:viande-bovine-francaise	en:france	NaN	0.0	Viande bovine origine France, barde naturelle ...	http://fr.openfoodfacts.org/images/products/20...	5.0	2.0	5.0	NaN	4.0	5.0	5.0	5.0	NaN	5.0	2.0	2.687529	NaN	-0.823909	-0.096910	NaN	1.313867
8201	2409382013680	1437826725	Foie de Bœuf (2 tranches)	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Sélectionné par nos Tripiers	selectionne-par-nos-tripiers	a	540.0	0.0	0.170000	1.2	NaN	20.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:high-proteins,fr:viande...	en:france	NaN	0.0	Foie de bœuf.	http://fr.openfoodfacts.org/images/products/24...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.732394	NaN	-0.769551	0.079181	NaN	1.301030
8270	2438435011021	1479709969	Foie de Bœuf	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Sans marque,Sélectionné par nos tripiers,Charal	sans-marque,selectionne-par-nos-tripiers,charal	a	540.0	0.0	0.170000	1.2	NaN	20.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,en:high-prote...	en:france	NaN	0.0	Foie de bœuf	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.732394	NaN	-0.769551	0.079181	NaN	1.301030
8271	2438438025780	1479485015	Rognon de Bœuf Cube	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Sans marque,Sélectionné par nos tripiers,Charal	sans-marque,selectionne-par-nos-tripiers,charal	a	389.0	0.0	0.450000	0.5	NaN	17.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,en:high-prote...	en:france	NaN	0.0	Rognons de bœuf origine France.	NaN	5.0	1.0	5.0	NaN	4.0	4.0	5.0	5.0	NaN	5.0	2.0	2.589950	NaN	-0.346787	-0.301030	NaN	1.230449
8410	2494593019613	1479305341	1 Onglet	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	641.0	0.0	0.190000	3.9	NaN	19.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,en:made-in-fr...	en:france	NaN	0.0	Viande bovine à griller 100%.	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	3.0	NaN	5.0	2.0	2.806858	NaN	-0.721246	0.591065	NaN	1.278754
9598	2695396018302	1479709986	Foie de veau X1	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Sans marque,Sélectionné par nos tripiers	sans-marque,selectionne-par-nos-tripiers	a	567.0	0.0	0.190000	1.6	NaN	20.0	Meat	Fish Meat Eggs	en:green-dot,en:made-in-france,fr:origine-france	en:france	NaN	0.0	Foie de veau	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.753583	NaN	-0.721246	0.204120	NaN	1.301030
20804	3181232120128	1447668641	Steacks Hachés Pur Boeuf	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	542.0	0.0	0.230000	2.3	NaN	21.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:100-muscle,fr:viande-bo...	en:france	NaN	0.0	Pure viande de boeuf hachée.	http://fr.openfoodfacts.org/images/products/31...	5.0	1.0	5.0	NaN	4.0	4.0	5.0	4.0	NaN	5.0	2.0	2.733999	NaN	-0.638272	0.361728	NaN	1.322219
20805	3181232120135	1465060545	Le Tradition Façon Bouchère	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	542.0	0.0	0.230000	2.3	NaN	21.0	Meat	Fish Meat Eggs	fr:viande-francaise,fr:viande-bovine-francaise	en:france	NaN	0.0	Pure viande de boeuf hachée. Pourcentage de ma...	NaN	5.0	1.0	5.0	NaN	4.0	4.0	5.0	4.0	NaN	5.0	2.0	2.733999	NaN	-0.638272	0.361728	NaN	1.322219
20841	3181232180504	1490274275	Carpacio charal	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	1000.0	0.5	0.290000	1.6	NaN	20.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,fr:viande-bov...	en:france,en:switzerland	en:e160c	1.0	Carpaccio mariné 87% : viande bovine 92%, mari...	http://fr.openfoodfacts.org/images/products/31...	5.0	5.0	4.0	5.0	4.0	4.0	5.0	4.0	NaN	5.0	2.0	3.000000	-0.30103	-0.537602	0.204120	NaN	1.301030
20860	3181232220071	1486418985	1 steak haché pur boeuf 5% M.G	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	544.0	0.0	0.199898	2.3	NaN	21.1	Meat	Fish Meat Eggs	fr:viande-francaise,en:100-muscle,fr:viande-bo...	en:france	NaN	0.0	100% viande hachée de boeuf.	http://fr.openfoodfacts.org/images/products/31...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.735599	NaN	-0.699192	0.361728	NaN	1.324282
20864	3181232220286	1489430480	2 Steaks Hachés pur Bœuf 5% de M.G.	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	542.0	0.0	0.230000	2.3	NaN	21.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,en:pure-beef,...	en:france	NaN	0.0	100% viande hachée de bœuf, taux de MG à 5%. C...	http://fr.openfoodfacts.org/images/products/31...	5.0	2.0	5.0	NaN	4.0	4.0	5.0	4.0	NaN	5.0	2.0	2.733999	NaN	-0.638272	0.361728	NaN	1.322219
20865	3181232220309	1482083405	Petit appétit 2 steaks hachés 5% pur boeuf	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	544.0	0.0	0.200000	2.3	NaN	21.1	Meat	Fish Meat Eggs	fr:viande-francaise,en:100-muscle,fr:viande-bo...	en:france	NaN	0.0	100% viande hachée de boeuf.	http://fr.openfoodfacts.org/images/products/31...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.735599	NaN	-0.698970	0.361728	NaN	1.324282
20876	3181232222037	1471692032	L'Authentique Façon Bouchère 5% M.G.	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	544.0	0.0	0.200000	2.3	NaN	21.1	Meat	Fish Meat Eggs	fr:viande-francaise,en:made-in-france,fr:viand...	en:france,en:switzerland	NaN	0.0	100% viande hachée de bœuf.	http://fr.openfoodfacts.org/images/products/31...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.735599	NaN	-0.698970	0.361728	NaN	1.324282
20910	3181238928681	1471551051	4 Pavés de Bœuf	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal,Charal Barbecue	charal,charal-barbecue	a	476.0	0.0	0.660000	0.8	NaN	23.0	Meat	Fish Meat Eggs	fr:viande-francaise,en:green-dot,fr:au-sel-de-...	en:france	NaN	0.0	Ingrédients : 100% viande de bœuf. Ingrédients...	NaN	5.0	5.0	4.0	NaN	4.0	4.0	5.0	5.0	NaN	5.0	2.0	2.677607	NaN	-0.180456	-0.096910	NaN	1.361728
20924	3181238931377	1467800970	Tendre de Boeuf x2	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	544.0	0.0	0.139954	2.3	NaN	20.0	Meat	Fish Meat Eggs	fr:viande-francaise,fr:viande-bovine-francaise	en:france	NaN	0.0	100% viande de boeuf.	http://fr.openfoodfacts.org/images/products/31...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.735599	NaN	-0.854015	0.361728	NaN	1.301030
20929	3181238940089	1450781949	Tendre de Boeuf (format spécial x6)	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Charal	charal	a	544.0	0.0	0.140000	2.3	NaN	20.0	Meat	Fish Meat Eggs	fr:saveurs-de-l-annee,fr:viande-francaise,fr:s...	en:france	NaN	0.0	100% Viande de boeuf.	http://fr.openfoodfacts.org/images/products/31...	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.735599	NaN	-0.853872	0.361728	NaN	1.301030
31186	3254565417804	1486492183	Steak haché pur boeuf façon bouchère	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Auchan	auchan	a	507.0	0.5	0.150000	1.6	NaN	19.6	Meat	Fish Meat Eggs	fr:viande-francaise,fr:viande-bovine-francaise	en:france	NaN	0.0	100 % viande de bœuf.	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	4.0	NaN	5.0	2.0	2.705008	-0.30103	-0.823909	0.204120	NaN	1.292256
38443	3263858656615	1480937202	Brochettes de Bœuf	en:to-be-checked,en:complete,en:nutrition-fact...	Viandes	Leader Price	leader-price	a	384.0	0.4	0.100000	0.5	NaN	15.3	Meat	Fish Meat Eggs	en:green-dot,en:made-in-france,fr:origine-france	en:france	NaN	0.0	Viande de bœuf origine France 65% - poivrons n...	NaN	5.0	1.0	5.0	NaN	4.0	5.0	5.0	5.0	NaN	5.0	2.0	2.584331	-0.39794	-1.000000	-0.301030	NaN	1.184691